Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the BerkeleyVision and LearningCenter (BVLC) and by a large community of open-source contributors. Caffe allows the user to define, train and deploy DeepNeural Networks (DNNs) through accessible command line, Python and MATLAB interfaces. Caffe is fast due to integrated GPU acceleration. In this class we will introduce the following aspects of Caffe, demonstrated through a practical DNN training and deployment example:
- Caffe program structure and core functionality
- Data management within Caffe
- DNN definition and training parameter selection
- Monitoring DNN training
- Deploying DNNs for classification or feature extraction

published:19 Aug 2015

views:76577

published:13 Jul 2016

views:2482

published:05 Feb 2019

views:391

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.
Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.
Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.
Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

published:18 Jan 2016

views:50714

The code with which you can run experiments is the following:
python python/classify.py --print_results examples/images/cat.jpg foo

published:13 Jan 2016

views:20357

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano
An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
Learn more about TF Learn here:
https://github.com/tflearn/tflearn
and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html
Learn more about TensorFlow here:
https://www.oreilly.com/learning/hello-tensorflow
More on Keras here:
http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
More on SciKit Learn here:
http://scikit-learn.org/stable/tutorial/
More on Caffe here:
http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html
More on Theano here:
https://github.com/Newmu/Theano-Tutorials
Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

published:01 Oct 2016

views:160761

cafe anatolia & arabia - musical journey beautiful music
songs...
Amr Diab-Osad Einy
Amr Ismail - DreamsArabic Music By AamirKangdaBillyEsteban - PassionDubai dream
Eternity Sonsuzluk Billy Esteban
Lena Chamamyan - Love In DamascusMercan Dede - Napas
Mohamed Rouane - SouvenirNasser Shibani Sweet Pain
Ruya music by Serkan Cagri
mohamed rouane
Billy Esteban - Rhythm Of Sand
All copyrights belong to the artists,
authors of songs and their record companies.
The photographs come from various sites of
Internet, also not belonging to me.
The above music video is not meant violations -
claims to have the record companies but intended ONLY FOR ENTERTAINMENT.
If immediately affected someone's interests, I am willing to make
the immediate and permanent deletion.

published:01 Jan 2017

views:7200227

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.
Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier
Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw
Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating
More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
http://neuralnetworksanddeeplearning.com/chap6.html
http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/
http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2
Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
Please subscribe! And like. And comment. That's what keeps me going.
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
The students, Rabiul, Mamun and Foyez are all from underpriviliged backgrounds and have been studying at CAFFE for a few years. So far they have learned a lot about Scratch but we felt it was time they got serious about coding.
We have decided to teach Lua and use it with Corona SDK as we have found this is a very effective way for beginners to be able to make their own mobile apps.
Foyez recently took part in a nation wide Game Jam where he helped design artwork for his team's mobile app. See them in action here: https://www.youtube.com/playlist?list=PLR_eGkq2qO5ZTFG_FbHWOPYpfQWSTpmGt
In this video, our 3 students spent the whole day bringing all they had learned together to create their own Role Playing Game for Android devices. The next day, they presented their work to other CAFFE students, who got to play-test their game. Afterwards we celebrated by taking the students to PizzaHit.

Hamming space

More generally, a Hamming space can be defined over any alphabet (set) Q as the set of words of a fixed length N with letters from Q. If Q is a finite field, then a Hamming space over Q is an N-dimensional vector space over Q. In the typical, binary case, the field is thus GF(2) (also denoted by Z2).

In coding theory, if Q has q elements, then any subsetC (usually assumed of cardinality at least two) of the N-dimensional Hamming space over Q is called a q-ary code of length N; the elements of C are called codewords. In the case where C is a linear subspace of its Hamming space, it is called a linear code. A typical example of linear code is the Hamming code. Codes defined via a Hamming space necessarily have the same length for every codeword, so they are called block codes when it is necessary to distinguish them from variable-length codes that are defined by unique factorization on a monoid.

Code (album)

Code (stylized as C O D E) is an album by British electronic band Cabaret Voltaire. The track "Don't Argue" was released as a single, as was "Here To Go".

The lyrics (and title) of "Don't Argue" incorporate verbatim a number of sentences from the narration of the 1945 short film Your Job in Germany, directed by Frank Capra. The film was aimed at American soldiers occupying Germany and strongly warned against trusting or fraternizing with German citizens.

Code (law)

A code is a type of legislation that purports to exhaustively cover a complete system of laws or a particular area of law as it existed at the time the code was enacted, by a process of codification. Though the process and motivations for codification are similar in different common law and civil law systems, their usage is different. In a civil law country, a Code typically exhaustively covers the complete system of law, such as civil law or criminal law. By contrast, in a common law country with legislative practices in the English tradition, a Code is a less common form of legislation, which differs from usual legislation that, when enacted, modify the existing common law only to the extent of its express or implicit provision, but otherwise leaves the common law intact. By contrast, a code entirely replaces the common law in a particular area, leaving the common law inoperative unless and until the code is repealed. In a third case of slightly different usage, in the United States and other common law countries that have adopted similar legislative practices, a Code is a standing body of statute law on a particular area, which is added to, subtracted from, or otherwise modified by individual legislative enactments.

Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervisedfeature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

NVIDIA Deep Learning Course: Class #3 - Getting started with Caffe

Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the BerkeleyVision and LearningCenter (BVLC) and by a large community of open-source contributors. Caffe allows the user to define, train and deploy DeepNeural Networks (DNNs) through accessible command line, Python and MATLAB interfaces. Caffe is fast due to integrated GPU acceleration. In this class we will introduce the following aspects of Caffe, demonstrated through a practical DNN training and deployment example:
- Caffe program structure and core functionality
- Data management within Caffe
- DNN definition and training parameter selection
- Monitoring DNN training
- Deploying DNNs for classification or feature extraction

2:35

CAFFE CLUB CODE Novi Travnik

CAFFE CLUB CODE Novi Travnik

CAFFE CLUB CODE Novi Travnik

0:38

Da sam pauk-Caffe Code

Da sam pauk-Caffe Code

Da sam pauk-Caffe Code

2:49

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.
Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.
Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.
Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

13:10

How to run experiments using Caffe on Ubuntu

How to run experiments using Caffe on Ubuntu

How to run experiments using Caffe on Ubuntu

The code with which you can run experiments is the following:
python python/classify.py --print_results examples/images/cat.jpg foo

5:45

Deep Learning Frameworks Compared

Deep Learning Frameworks Compared

Deep Learning Frameworks Compared

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano
An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
Learn more about TF Learn here:
https://github.com/tflearn/tflearn
and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html
Learn more about TensorFlow here:
https://www.oreilly.com/learning/hello-tensorflow
More on Keras here:
http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
More on SciKit Learn here:
http://scikit-learn.org/stable/tutorial/
More on Caffe here:
http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html
More on Theano here:
https://github.com/Newmu/Theano-Tutorials
Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

1:03:51

Cafe anatolia & arabia - musical journey beautiful music

Cafe anatolia & arabia - musical journey beautiful music

Cafe anatolia & arabia - musical journey beautiful music

cafe anatolia & arabia - musical journey beautiful music
songs...
Amr Diab-Osad Einy
Amr Ismail - DreamsArabic Music By AamirKangdaBillyEsteban - PassionDubai dream
Eternity Sonsuzluk Billy Esteban
Lena Chamamyan - Love In DamascusMercan Dede - Napas
Mohamed Rouane - SouvenirNasser Shibani Sweet Pain
Ruya music by Serkan Cagri
mohamed rouane
Billy Esteban - Rhythm Of Sand
All copyrights belong to the artists,
authors of songs and their record companies.
The photographs come from various sites of
Internet, also not belonging to me.
The above music video is not meant violations -
claims to have the record companies but intended ONLY FOR ENTERTAINMENT.
If immediately affected someone's interests, I am willing to make
the immediate and permanent deletion.

8:45

How to Make an Image Classifier - Intro to Deep Learning #6

How to Make an Image Classifier - Intro to Deep Learning #6

How to Make an Image Classifier - Intro to Deep Learning #6

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.
Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier
Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw
Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating
More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
http://neuralnetworksanddeeplearning.com/chap6.html
http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/
http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2
Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
Please subscribe! And like. And comment. That's what keeps me going.
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

CAFFE Code Camp Final

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
The students, Rabiul, Mamun and Foyez are all from underpriviliged backgrounds and have been studying at CAFFE for a few years. So far they have learned a lot about Scratch but we felt it was time they got serious about coding.
We have decided to teach Lua and use it with Corona SDK as we have found this is a very effective way for beginners to be able to make their own mobile apps.
Foyez recently took part in a nation wide Game Jam where he helped design artwork for his team's mobile app. See them in action here: https://www.youtube.com/playlist?list=PLR_eGkq2qO5ZTFG_FbHWOPYpfQWSTpmGt
In this video, our 3 students spent the whole day bringing all they had learned together to create their own Role Playing Game for Android devices. The next day, they presented their work to other CAFFE students, who got to play-test their game. Afterwards we celebrated by taking the students to PizzaHit.

NVIDIA Deep Learning Course: Class #3 - Getting started with Caffe

Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the BerkeleyVision and LearningCenter (BVLC) and by a large community of open-source contributors. Caffe allows the user to define, train and deploy DeepNeural Networks (DNNs) through accessible command line, Python and MATLAB interfaces. Caffe is fast due to integrated GPU acceleration. In this class we will introduce the following aspects of Caffe, demonstrated through a practical DNN training and deployment example:
- Caffe program structure and core functionality
- Data management within Caffe
- DNN definition and training parameter selection
- Monitoring DNN training
- Deploying DNNs for classification or feature extraction

published: 19 Aug 2015

CAFFE CLUB CODE Novi Travnik

published: 13 Jul 2016

Da sam pauk-Caffe Code

published: 05 Feb 2019

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.
Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up ...

published: 18 Jan 2016

How to run experiments using Caffe on Ubuntu

The code with which you can run experiments is the following:
python python/classify.py --print_results examples/images/cat.jpg foo

published: 13 Jan 2016

Deep Learning Frameworks Compared

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano
An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
Learn more about TF Learn here:
https://github.com/tflearn/tflearn
and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html
Learn more about TensorFlow here:
https://www.oreilly.com/learning...

published: 01 Oct 2016

Cafe anatolia & arabia - musical journey beautiful music

cafe anatolia & arabia - musical journey beautiful music
songs...
Amr Diab-Osad Einy
Amr Ismail - DreamsArabic Music By AamirKangdaBillyEsteban - PassionDubai dream
Eternity Sonsuzluk Billy Esteban
Lena Chamamyan - Love In DamascusMercan Dede - Napas
Mohamed Rouane - SouvenirNasser Shibani Sweet Pain
Ruya music by Serkan Cagri
mohamed rouane
Billy Esteban - Rhythm Of Sand
All copyrights belong to the artists,
authors of songs and their record companies.
The photographs come from various sites of
Internet, also not belonging to me.
The above music video is not meant violations -
claims to have the record companies but intended ONLY FOR ENTERTAINMENT.
If immediately affected someone's interests, I am willing to make
the immediate and permanent deletion.

published: 01 Jan 2017

How to Make an Image Classifier - Intro to Deep Learning #6

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.
Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier
Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw
Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating
More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/...

CAFFE Code Camp Final

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
The students, Rabiul, Mamun and Foyez are all from underpriviliged backgrounds and have been studying at CAFFE for a few years. So far they have learned a lot about Scratch but we felt it was time they got serious about coding.
We have decided to teach Lua and use it with Corona SDK as we have found this is a very effective way for beginners to be able to make their own mobile apps.
Foyez recently took part in a nation wide Game Jam where he helped design artwork for his team's mobile app. See them in action here: https://www.youtube.com/playlist?list=PLR_eGkq2qO5ZTFG_FbHWOPYpfQWSTpmGt
In this video, our 3 students spent the whole...

NVIDIA Deep Learning Course: Class #3 - Getting started with Caffe

Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the Ber...

Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the BerkeleyVision and LearningCenter (BVLC) and by a large community of open-source contributors. Caffe allows the user to define, train and deploy DeepNeural Networks (DNNs) through accessible command line, Python and MATLAB interfaces. Caffe is fast due to integrated GPU acceleration. In this class we will introduce the following aspects of Caffe, demonstrated through a practical DNN training and deployment example:
- Caffe program structure and core functionality
- Data management within Caffe
- DNN definition and training parameter selection
- Monitoring DNN training
- Deploying DNNs for classification or feature extraction

Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the BerkeleyVision and LearningCenter (BVLC) and by a large community of open-source contributors. Caffe allows the user to define, train and deploy DeepNeural Networks (DNNs) through accessible command line, Python and MATLAB interfaces. Caffe is fast due to integrated GPU acceleration. In this class we will introduce the following aspects of Caffe, demonstrated through a practical DNN training and deployment example:
- Caffe program structure and core functionality
- Data management within Caffe
- DNN definition and training parameter selection
- Monitoring DNN training
- Deploying DNNs for classification or feature extraction

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configur...

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.
Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.
Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.
Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.
Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.
Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.
Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Deep Learning Frameworks Compared

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of...

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano
An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
Learn more about TF Learn here:
https://github.com/tflearn/tflearn
and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html
Learn more about TensorFlow here:
https://www.oreilly.com/learning/hello-tensorflow
More on Keras here:
http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
More on SciKit Learn here:
http://scikit-learn.org/stable/tutorial/
More on Caffe here:
http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html
More on Theano here:
https://github.com/Newmu/Theano-Tutorials
Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
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Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano
An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
Learn more about TF Learn here:
https://github.com/tflearn/tflearn
and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html
Learn more about TensorFlow here:
https://www.oreilly.com/learning/hello-tensorflow
More on Keras here:
http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
More on SciKit Learn here:
http://scikit-learn.org/stable/tutorial/
More on Caffe here:
http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html
More on Theano here:
https://github.com/Newmu/Theano-Tutorials
Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

cafe anatolia & arabia - musical journey beautiful music
songs...
Amr Diab-Osad Einy
Amr Ismail - DreamsArabic Music By AamirKangdaBillyEsteban - PassionDubai dream
Eternity Sonsuzluk Billy Esteban
Lena Chamamyan - Love In DamascusMercan Dede - Napas
Mohamed Rouane - SouvenirNasser Shibani Sweet Pain
Ruya music by Serkan Cagri
mohamed rouane
Billy Esteban - Rhythm Of Sand
All copyrights belong to the artists,
authors of songs and their record companies.
The photographs come from various sites of
Internet, also not belonging to me.
The above music video is not meant violations -
claims to have the record companies but intended ONLY FOR ENTERTAINMENT.
If immediately affected someone's interests, I am willing to make
the immediate and permanent deletion.

cafe anatolia & arabia - musical journey beautiful music
songs...
Amr Diab-Osad Einy
Amr Ismail - DreamsArabic Music By AamirKangdaBillyEsteban - PassionDubai dream
Eternity Sonsuzluk Billy Esteban
Lena Chamamyan - Love In DamascusMercan Dede - Napas
Mohamed Rouane - SouvenirNasser Shibani Sweet Pain
Ruya music by Serkan Cagri
mohamed rouane
Billy Esteban - Rhythm Of Sand
All copyrights belong to the artists,
authors of songs and their record companies.
The photographs come from various sites of
Internet, also not belonging to me.
The above music video is not meant violations -
claims to have the record companies but intended ONLY FOR ENTERTAINMENT.
If immediately affected someone's interests, I am willing to make
the immediate and permanent deletion.

How to Make an Image Classifier - Intro to Deep Learning #6

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive int...

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.
Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier
Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw
Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating
More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
http://neuralnetworksanddeeplearning.com/chap6.html
http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/
http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2
Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
Please subscribe! And like. And comment. That's what keeps me going.
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.
Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier
Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw
Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating
More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
http://neuralnetworksanddeeplearning.com/chap6.html
http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/
http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2
Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
Please subscribe! And like. And comment. That's what keeps me going.
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

CAFFE Code Camp Final

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
T...

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
The students, Rabiul, Mamun and Foyez are all from underpriviliged backgrounds and have been studying at CAFFE for a few years. So far they have learned a lot about Scratch but we felt it was time they got serious about coding.
We have decided to teach Lua and use it with Corona SDK as we have found this is a very effective way for beginners to be able to make their own mobile apps.
Foyez recently took part in a nation wide Game Jam where he helped design artwork for his team's mobile app. See them in action here: https://www.youtube.com/playlist?list=PLR_eGkq2qO5ZTFG_FbHWOPYpfQWSTpmGt
In this video, our 3 students spent the whole day bringing all they had learned together to create their own Role Playing Game for Android devices. The next day, they presented their work to other CAFFE students, who got to play-test their game. Afterwards we celebrated by taking the students to PizzaHit.

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
The students, Rabiul, Mamun and Foyez are all from underpriviliged backgrounds and have been studying at CAFFE for a few years. So far they have learned a lot about Scratch but we felt it was time they got serious about coding.
We have decided to teach Lua and use it with Corona SDK as we have found this is a very effective way for beginners to be able to make their own mobile apps.
Foyez recently took part in a nation wide Game Jam where he helped design artwork for his team's mobile app. See them in action here: https://www.youtube.com/playlist?list=PLR_eGkq2qO5ZTFG_FbHWOPYpfQWSTpmGt
In this video, our 3 students spent the whole day bringing all they had learned together to create their own Role Playing Game for Android devices. The next day, they presented their work to other CAFFE students, who got to play-test their game. Afterwards we celebrated by taking the students to PizzaHit.

NVIDIA Deep Learning Course: Class #3 - Getting started with Caffe

Register for the full course and find the Q&A log at https://developer.nvidia.com/deep-learning-courses
Caffe is a Deep Learning framework developed by the BerkeleyVision and LearningCenter (BVLC) and by a large community of open-source contributors. Caffe allows the user to define, train and deploy DeepNeural Networks (DNNs) through accessible command line, Python and MATLAB interfaces. Caffe is fast due to integrated GPU acceleration. In this class we will introduce the following aspects of Caffe, demonstrated through a practical DNN training and deployment example:
- Caffe program structure and core functionality
- Data management within Caffe
- DNN definition and training parameter selection
- Monitoring DNN training
- Deploying DNNs for classification or feature extraction

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

Caffe is a Deep Learning library that is well suited for machine vision and forecasting applications. With Caffe you can build a net with sophisticated configuration options, and you can access premade nets in an online community.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
Caffe is a C++/CUDA library that was developed by Yangqing Jia of Google. The library was initially designed for machine vision tasks, but recent versions support sequences, speech and text, and reinforcement learning. Since it’s built on top of CUDA, Caffe supports the use of GPUs.
Caffe allows the user to configure the hyper-parameters of a deep net. The layer configuration options are robust and sophisticated – individual layers can be set up as vision layers, loss layers, activation layers, and many others. Caffe’s community website allows users to contribute premade deep nets along with other useful resources.
Vectorization is achieved through specialized arrays called “blobs”, which help optimize the computational costs of various operations.
Have you ever used the Caffe library in one of your Deep Net projects? Please comment and share your experiences.
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Deep Learning Frameworks Compared

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.
The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano
An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
Learn more about TF Learn here:
https://github.com/tflearn/tflearn
and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html
Learn more about TensorFlow here:
https://www.oreilly.com/learning/hello-tensorflow
More on Keras here:
http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
More on SciKit Learn here:
http://scikit-learn.org/stable/tutorial/
More on Caffe here:
http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html
More on Theano here:
https://github.com/Newmu/Theano-Tutorials
Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Cafe anatolia & arabia - musical journey beautiful music

cafe anatolia & arabia - musical journey beautiful music
songs...
Amr Diab-Osad Einy
Amr Ismail - DreamsArabic Music By AamirKangdaBillyEsteban - PassionDubai dream
Eternity Sonsuzluk Billy Esteban
Lena Chamamyan - Love In DamascusMercan Dede - Napas
Mohamed Rouane - SouvenirNasser Shibani Sweet Pain
Ruya music by Serkan Cagri
mohamed rouane
Billy Esteban - Rhythm Of Sand
All copyrights belong to the artists,
authors of songs and their record companies.
The photographs come from various sites of
Internet, also not belonging to me.
The above music video is not meant violations -
claims to have the record companies but intended ONLY FOR ENTERTAINMENT.
If immediately affected someone's interests, I am willing to make
the immediate and permanent deletion.

How to Make an Image Classifier - Intro to Deep Learning #6

We're going to make our own ImageClassifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.
Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier
Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw
Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating
More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
http://neuralnetworksanddeeplearning.com/chap6.html
http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/
http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2
Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
Please subscribe! And like. And comment. That's what keeps me going.
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

CAFFE Code Camp Final

We are holding a coding camp for 3 talented students. Our aim is to take them from writing "Hello World" to developing a fully fledged mobile game in a week.
The students, Rabiul, Mamun and Foyez are all from underpriviliged backgrounds and have been studying at CAFFE for a few years. So far they have learned a lot about Scratch but we felt it was time they got serious about coding.
We have decided to teach Lua and use it with Corona SDK as we have found this is a very effective way for beginners to be able to make their own mobile apps.
Foyez recently took part in a nation wide Game Jam where he helped design artwork for his team's mobile app. See them in action here: https://www.youtube.com/playlist?list=PLR_eGkq2qO5ZTFG_FbHWOPYpfQWSTpmGt
In this video, our 3 students spent the whole day bringing all they had learned together to create their own Role Playing Game for Android devices. The next day, they presented their work to other CAFFE students, who got to play-test their game. Afterwards we celebrated by taking the students to PizzaHit.

Hamming space

More generally, a Hamming space can be defined over any alphabet (set) Q as the set of words of a fixed length N with letters from Q. If Q is a finite field, then a Hamming space over Q is an N-dimensional vector space over Q. In the typical, binary case, the field is thus GF(2) (also denoted by Z2).

In coding theory, if Q has q elements, then any subsetC (usually assumed of cardinality at least two) of the N-dimensional Hamming space over Q is called a q-ary code of length N; the elements of C are called codewords. In the case where C is a linear subspace of its Hamming space, it is called a linear code. A typical example of linear code is the Hamming code. Codes defined via a Hamming space necessarily have the same length for every codeword, so they are called block codes when it is necessary to distinguish them from variable-length codes that are defined by unique factorization on a monoid.